Basmati Rice Quality Enhancement by Zinc Fertilization and Green Manuring on a Sub-tropical Inceptisol in Indo-Gangetic Plains of India
Notice bibliographique
Résumé
Basmati (aromatic) rice is premier rice grown in north-western India and Pakistan. This rice is preferred for their long and slender kernels which expand 3-4 times in length and remain fluffy and are well known all over the world, especially in the Middle East and South Asia for their long fluffy grains on cooking. Paddy soils are usually deficient in organic matter because of high temperature and moisture, which causes rapid decomposition of organic matter. The importance of leguminous green manure crops in improving soil fertility, and soil physical properties received increasing attention. Also, the zinc (Zn) deficiency in soils is prevalent worldwide, especially in high pH calcareous soils. No reports were available on combining green manuring crops and Zn fertilization on productivity, Zn content and kernel quality of Basmati rice. Therefore, the current investigation was undertaken to quantify the combined effects of summer green manuring crops and zinc fertilization on productivity, Zn content and kernel quality of Basmati rice in summer green manuring-Basmati rice cropping system. A field study was therefore conducted for two years (2009 and 2010) on a sandy clay-loam soil (typic Ustochrept) at the research farm of the ICAR-Indian Agricultural Research Institute, New Delhi, India. The experiments were conducted in split plot design, keeping three green manuring crops viz. Sesbania aculeata (Dhaincha), Crotalaria juncea (Sunhemp), and Vigna unguiculata (Cowpea) and one summer fallow treatment as main-plot treatments and six Zn sources viz. control (no Zn application), ZnSO4∙7H2O (21% Zn), ZnSO4∙H2O (33% Zn), ZnO (82% Zn), ZnSO4∙7H2O + ZnO (50% + 50%) and EDTA-chelated Zn (12% Zn) in sub-plots and was replicated thrice. The experiments in both the years were conducted with a fixed lay-out plan on the same site. The results showed that incorporation of green manures along with zinc (Zn) fertilization increased grain and straw yield, enhanced Zn concentrations and improved the kernel quality before and after cooking in Basmati rice ‘Pusa Basmati 1’. The application of EDTA-chelated Zn (12% Zn) was the best in terms of grain and straw yield and Zn concentrations in grain and straw and kernel quality before and after cooking Basmati rice. Application of ZnSO4∙7H2O (21% Zn) was the second-best treatment followed by ZnSO4∙H2O (33% Zn) and ZnSO4∙7H2O + ZnO (50% + 50%). Application of ZnO (82% Zn) had least effect in increasing the studied parameters. The lowest values were observed with control (no Zn application). Among the summer green manuring crops, incorporation of Sesbania aculeata (Dhaincha) was found to be the best over Crotalaria juncea (Sunhemp), Vigna unguiculata (Cowpea) and summer fallow in terms of grain and straw yield, Zn concentrations in grain and straw and kernel quality before and after cooking in Basmati rice. Zn fertilization with EDTA-chelated Zn (12% Zn) lead to 25.91 and 21.26% higher grain yield; 60.66 and 82.14% Zn-denser grains; with 13.33 and 10.92% increase in head rice recovery in Basmati rice over control (no Zn application) during 2009 and 2010, respectively.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».